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library(SingleCellExperiment)

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    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce,
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Welcome to Bioconductor

    Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor,
    see 'citation("Biobase")', and for packages 'citation("pkgname")'.

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library(scmap)

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library(Seurat)

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library(xgboost)

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library(Matrix)

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set.seed(1)
source("tianfengRwrappers.R")

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NOTE: Either Arial Narrow or Roboto Condensed fonts are required to use these themes.
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clusterProfiler v3.14.3  For help: https://guangchuangyu.github.io/software/clusterProfiler

If you use clusterProfiler in published research, please cite:
Guangchuang Yu, Li-Gen Wang, Yanyan Han, Qing-Yu He. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology. 2012, 16(5):284-287.

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========================================
circlize version 0.4.13
CRAN page: https://cran.r-project.org/package=circlize
Github page: https://github.com/jokergoo/circlize
Documentation: https://jokergoo.github.io/circlize_book/book/

If you use it in published research, please cite:
Gu, Z. circlize implements and enhances circular visualization
  in R. Bioinformatics 2014.

This message can be suppressed by:
  suppressPackageStartupMessages(library(circlize))
========================================

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========================================
ComplexHeatmap version 2.2.0
Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
Github page: https://github.com/jokergoo/ComplexHeatmap
Documentation: http://jokergoo.github.io/ComplexHeatmap-reference

If you use it in published research, please cite:
Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional 
  genomic data. Bioinformatics 2016.
========================================


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构造ref ds0

ds0 <- readRDS("ds0.rds")
ref_sce <- as.SingleCellExperiment(ds0)
logcounts(ref_sce) <- log2(counts(ref_sce) + 1)

counts(ref_sce) <- as.matrix(counts(ref_sce))
# normcounts(ref_sce) <- as.matrix(normcounts(ref_sce))
logcounts(ref_sce) <- as.matrix(logcounts(ref_sce))

rowData(ref_sce)$feature_symbol <- rownames(ref_sce)
ref_sce <- ref_sce[!duplicated(rownames(ref_sce)), ]
ref_sce <- selectFeatures(ref_sce, suppress_plot = FALSE)

Warning: 'isSpike' is deprecated.
See help("Deprecated")

ref_sce <- indexCell(ref_sce)

Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)

环回ds0

scmapCell_results <- scmapCell(ref_sce, list(ds0 = metadata(ref_sce)$scmap_cell_index))
scmapCell_clusters <- scmapCell2Cluster(
  scmapCell_results, 
  list(as.character(colData(ref_sce)$Classification1)))

temp <- data.frame(scmapCell_clusters$scmap_cluster_labs[,"ds0"],row.names = colnames(ds0))
Idents(ds0) <- temp
ggsave("./scmap/scmap_ds0tods0.svg", device = svg, width = 6, height = 4, plot = umapplot(ds0))

fig <- plot_ly(data.frame(table(temp)), labels = ~temp, values = ~Freq, type = 'pie',
        textposition = 'inside',
        textinfo = 'label+percent+value',
        insidetextfont = list(color = '#000000'),
        hoverinfo = 'text',
        text = ~paste0('cell numbers: ', Freq),
        marker = list(colors = colors_list,
                      line = list(color = '#FFFFFF', width = 0)),
        showlegend = FALSE) %>% layout(title = 'scmap_ds0tods0',
         xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
         yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE), 
         font = list(family = "Arial", size = 25, color = "black"))

fig

计算ARI = 0.8925608

mclust::adjustedRandIndex(temp[,1], ds0$Classification1)

[1] 0.8925608

confusing matrix

conmat <- table(as.character(ds0$Classification1), temp[,1], dnn=c("true","pre"))
conmat_prop <- prop.table(conmat, 1)
conmat_prop

              pre
true             Fibroblast Fibromyocyte     Pericyte          SMC   unassigned      Unknown
  Fibroblast   0.9745106964 0.0009103323 0.0000000000 0.0000000000 0.0241238052 0.0004551661
  Fibromyocyte 0.0086419753 0.7777777778 0.0012345679 0.0086419753 0.2037037037 0.0000000000
  Pericyte     0.0016090105 0.0048270314 0.9452936444 0.0016090105 0.0466613033 0.0000000000
  SMC          0.0000000000 0.0173010381 0.0057670127 0.8223760092 0.1545559400 0.0000000000
  Unknown      0.0833333333 0.0000000000 0.0000000000 0.0000000000 0.1715686275 0.7450980392

confuse_bubblemat(conmat_prop, rownames(conmat_prop),  colnames(conmat_prop),"ds0_scmap")

XGBoost

Idents(ds0) <- ds0$Classification1
ds0 <- RenameIdents(ds0, 'Fibroblast' = 0, 'SMC' = 1, 'Fibromyocyte' = 2, 'Pericyte' = 3, 'Unknown' = 4)
umapplot(ds0)

ds0_data <- get_data_table(ds0, highvar = F, type = "data")
ds0_label <- as.numeric(as.character(Idents(ds0)))

set.seed(7)
index <- c(1:dim(ds0_data)[2]) %>% sample(ceiling(0.3*dim(ds0_data)[2]), replace = F, prob = NULL)

colnames(ds0_data) <- NULL

ds0_train_data <- list(data = t(as(ds0_data[,-index],"dgCMatrix")), label = ds0_label[-index])
ds0_test_data <- list(data = t(as(ds0_data[,index],"dgCMatrix")), label = ds0_label[index])

ds0_train <- xgb.DMatrix(data = ds0_train_data$data,label = ds0_train_data$label)
ds0_test <- xgb.DMatrix(data = ds0_test_data$data,label = ds0_test_data$label)


watchlist <- list(train = ds0_train, eval = ds0_test)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds0))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, ds0_train, nrounds = 100, watchlist, verbose = 0)

predict_ds0_test <- round(predict(bst_model, newdata = ds0_test))
ds0_confuse_matrix_test <- table(ds0_test_data$label, predict_ds0_test, dnn=c("true","pre"))
ds0_confuse_matrix_test_prop <- prop.table(ds0_confuse_matrix_test, 1)
ds0_confuse_matrix_test_prop

    pre
true           0           1           2           3           4
   0 0.989345510 0.000000000 0.004566210 0.000000000 0.006088280
   1 0.000000000 0.923344948 0.052264808 0.024390244 0.000000000
   2 0.025316456 0.042194093 0.919831224 0.012658228 0.000000000
   3 0.000000000 0.002849003 0.005698006 0.991452991 0.000000000
   4 0.076923077 0.000000000 0.015384615 0.000000000 0.907692308

confuse_bubblemat(ds0_confuse_matrix_test_prop, c("Fibroblast", "SMC", "Fibromyocyte", "Pericyte", "Unknown"), c("Fibroblast", "SMC", "Fibromyocyte", "Pericyte", "Unknown"),"ds0_pretrain")

adjustedRandIndex(ds0_test_data$label, predict_ds0_test) #分类器性能

[1] 0.9316151

构造query

set.seed(1)
ds2 <- readRDS("ds2.rds")
query_sce <- as.SingleCellExperiment(ds2)
logcounts(query_sce) <- log2(counts(query_sce) + 1)

counts(query_sce) <- as.matrix(counts(query_sce))
# normcounts(query_sce) <- as.matrix(normcounts(query_sce))
logcounts(query_sce) <- as.matrix(logcounts(query_sce))

rowData(query_sce)$feature_symbol <- rownames(query_sce)
query_sce <- query_sce[!duplicated(rownames(query_sce)), ]
query_sce <- selectFeatures(query_sce, suppress_plot = FALSE)

Warning: 'isSpike' is deprecated.
See help("Deprecated")

query_sce <- indexCell(query_sce)

Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)

环回ds2

scmapCell_results <- scmapCell(query_sce, list(ds2 = metadata(query_sce)$scmap_cell_index))
scmapCell_clusters <- scmapCell2Cluster(
  scmapCell_results, 
  list(as.character(colData(query_sce)$Classification1)))

temp <- data.frame(scmapCell_clusters$scmap_cluster_labs[,"ds2"],row.names = colnames(ds2))
Idents(ds2) <- temp
ggsave("./scmap/scmap_ds2tods2.svg", device = svg, width = 6, height = 4, plot = umapplot(ds2))

fig <- plot_ly(data.frame(table(temp)), labels = ~temp, values = ~Freq, type = 'pie',
        textposition = 'inside',
        textinfo = 'label+percent+value',
        insidetextfont = list(color = '#000000'),
        hoverinfo = 'text',
        text = ~paste0('cell numbers: ', Freq),
        marker = list(colors = colors_list,
                      line = list(color = '#FFFFFF', width = 0)),
        showlegend = FALSE) %>% layout(title = 'scmap_ds2tods2',
         xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
         yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE), 
         font = list(family = "Arial", size = 25, color = "black"))

fig

计算ARI = 0.776329

mclust::adjustedRandIndex(temp[,1], ds2$Classification1)

[1] 0.776329

confusing matrix ds2

conmat <- table(as.character(ds2$Classification1), temp[,1], dnn=c("true","pre"))
conmat_prop <- prop.table(conmat, 1)
conmat_prop

              pre
true             Fibroblast Fibromyocyte     Pericyte         SMC1         SMC2   unassigned
  Fibroblast   0.9807846277 0.0032025620 0.0000000000 0.0000000000 0.0000000000 0.0160128102
  Fibromyocyte 0.0031222123 0.7814451383 0.0049063336 0.0107047279 0.0026761820 0.1971454059
  Pericyte     0.0000000000 0.0101073910 0.8180669615 0.0063171194 0.0000000000 0.1655085281
  SMC1         0.0000000000 0.0058292043 0.0040804430 0.8837073739 0.0040804430 0.1023025357
  SMC2         0.0009689922 0.0000000000 0.0009689922 0.0096899225 0.8226744186 0.1656976744

XGBoost ds2

Idents(ds2) <- ds2$Classification1
ds2 <- RenameIdents(ds2, 'SMC1' = 0, 'Fibromyocyte' = 1, 'Pericyte' = 2, 'Fibroblast' = 3, 'SMC2' = 4)

ds2_data <- get_data_table(ds2, highvar = F, type = "data")
ds2_label <- as.numeric(as.character(Idents(ds2)))

set.seed(7)
index <- c(1:dim(ds2_data)[2]) %>% sample(ceiling(0.3*dim(ds2_data)[2]), replace = F, prob = NULL)
colnames(ds2_data) <- NULL
ds2_train_data <- list(data = t(as(ds2_data[,-index],"dgCMatrix")), label = ds2_label[-index])
ds2_test_data <- list(data = t(as(ds2_data[,index],"dgCMatrix")), label = ds2_label[index])

ds2_train <- xgb.DMatrix(data = ds2_train_data$data,label = ds2_train_data$label)
ds2_test <- xgb.DMatrix(data = ds2_test_data$data,label = ds2_test_data$label)


watchlist <- list(train = ds2_train, eval = ds2_test)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, ds2_train, nrounds = 100, watchlist, verbose = 0)

ds2_confuse_matrix_test_prop

    pre
true           0           1           2           3           4
   0 0.964843750 0.017578125 0.011718750 0.000000000 0.005859375
   1 0.021865889 0.963556851 0.005830904 0.002915452 0.005830904
   2 0.037946429 0.033482143 0.928571429 0.000000000 0.000000000
   3 0.000000000 0.012886598 0.000000000 0.987113402 0.000000000
   4 0.012658228 0.025316456 0.000000000 0.000000000 0.962025316

ds2 project to ds0

scmapCell_results <- scmapCell(query_sce, list(ds0 = metadata(ref_sce)$scmap_cell_index))
scmapCell_clusters <- scmapCell2Cluster(
  scmapCell_results, 
  list(as.character(colData(ref_sce)$Classification1)))

temp <- data.frame(scmapCell_clusters$scmap_cluster_labs[,"ds0"],row.names = colnames(ds2))
Idents(ds2) <- temp
ggsave("./scmap/scmap_ds2tods0.svg", device = svg, width = 6, height = 4, plot = umapplot(ds2))

fig <- plot_ly(data.frame(table(temp)), labels = ~temp, values = ~Freq, type = 'pie',
        textposition = 'inside',
        textinfo = 'label+percent+value',
        insidetextfont = list(color = '#000000'),
        hoverinfo = 'text',
        text = ~paste0('cell numbers: ', Freq),
        marker = list(colors = colors_list,
                      line = list(color = '#FFFFFF', width = 0)),
        showlegend = FALSE) %>% layout(title = 'scmap_ds2tods0',
         xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
         yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE), 
         font = list(family = "Arial", size = 25, color = "black"))

fig

ds0FbM <- subset(ds0,ident = "Fibromyocyte")
ds2FbM <- subset(ds2,ident = "Fibromyocyte")

ds0data <- get_data_table(ds0FbM,type = "data")
ds2data <- get_data_table(ds2FbM,type = "data")

# genes_to_show <- c("IGFBP2","MGP","MYH11","DCN","TNFRSF11B")
genes_to_show <- c("DCN","LUM","MMP2","ACTA2","TNFRSF11B","FBLN1")

merge_expr <- data.frame()

for (i in lapply(genes_to_show, func1,"ds0",ds0data))
{
  merge_expr <- rbind(merge_expr,i)
}
for (i in lapply(genes_to_show, func1,"ds2",ds2data))
{
  merge_expr <- rbind(merge_expr,i)
}

rownames(merge_expr) <- NULL

Data_summary <- Rmisc::summarySE(merge_expr, measurevar="expr", groupvars=c("sample","gene"))
head(Data_summary)

ggobj <- ggplot(merge_expr,aes(x = gene, y = expr,fill = sample)) +
  geom_split_violin(trim= F, color="white", scale = "area") + 
  geom_point(data = Data_summary,aes(x = gene, y= expr), pch=19,
             position=position_dodge(0.2),size= 1) + #绘制均值位置
  geom_errorbar(data = Data_summary, aes(ymin = expr-ci, ymax= expr+ci), 
                width= 0.05, 
                position= position_dodge(0.2), #误差线位置，和均值位置相匹配
                color="black",
                alpha = 0.7,
                size= 0.5) +
  scale_fill_manual(values = c("#b1d6fb", "#fd9999"))+ 
  labs(y=("Log2 expression"),x=NULL,title = "Split violin") + 
  theme_classic()+ mytheme + stat_compare_means(aes(group = sample),
                     label = "p.format",
                     method = "wilcox.test",
                     label.y = max(merge_expr$expr),
                      hide.ns = F)
ggobj
ggsave("./scmap/scmapsupds0tods2.svg", device = svg, plot = ggobj, height = 6, width = 10)

ds0 project to ds2

构造ref

ds2 <- readRDS("ds2.rds")
ref_sce <- as.SingleCellExperiment(ds2)
logcounts(ref_sce) <- log2(counts(ref_sce) + 1)

counts(ref_sce) <- as.matrix(counts(ref_sce))
# normcounts(ref_sce) <- as.matrix(normcounts(ref_sce))
logcounts(ref_sce) <- as.matrix(logcounts(ref_sce))

rowData(ref_sce)$feature_symbol <- rownames(ref_sce)
ref_sce <- ref_sce[!duplicated(rownames(ref_sce)), ]
ref_sce <- selectFeatures(ref_sce, suppress_plot = FALSE)
ref_sce <- indexCell(ref_sce)

构造query

ds0 <- readRDS("ds0.rds")
query_sce <- as.SingleCellExperiment(ds0)
logcounts(query_sce) <- log2(counts(query_sce) + 1)

counts(query_sce) <- as.matrix(counts(query_sce))
# normcounts(query_sce) <- as.matrix(normcounts(query_sce))
logcounts(query_sce) <- as.matrix(logcounts(query_sce))

rowData(query_sce)$feature_symbol <- rownames(query_sce)
query_sce <- query_sce[!duplicated(rownames(query_sce)), ]
query_sce <- selectFeatures(query_sce, suppress_plot = FALSE)
query_sce <- indexCell(query_sce)

scmapCell_results <- scmapCell(query_sce, list(ds2 = metadata(ref_sce)$scmap_cell_index))
scmapCell_clusters <- scmapCell2Cluster(
  scmapCell_results, 
  list(as.character(colData(ref_sce)$Classification1)))
temp <- data.frame(scmapCell_clusters$scmap_cluster_labs[,"ds2"], row.names = colnames(ds0))
Idents(ds0) <- temp
ggsave("./scmap/scmap_ds0tods2.svg", device = svg, width = 6, height = 4, plot = umapplot(ds0))

fig <- plot_ly(data.frame(table(temp)), labels = ~temp, values = ~Freq, type = 'pie',
        textposition = 'inside',
        textinfo = 'label+percent+value',
        insidetextfont = list(color = '#000000'),
        hoverinfo = 'text',
        text = ~paste0('cell numbers: ', Freq),
        marker = list(colors = colors_list,
                      line = list(color = '#FFFFFF', width = 0)),
        showlegend = FALSE) %>% layout(title = 'scmap_ds0tods2',
         xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
         yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE), 
         font = list(family = "Arial", size = 25, color = "black"))

fig

ds0FbM <- subset(ds0,ident = "Fibromyocyte")
ds2FbM <- subset(ds2,ident = "Fibromyocyte")

ds0data <- get_data_table(ds0FbM,type = "data")
ds2data <- get_data_table(ds2FbM,type = "data")

# genes_to_show <- c("IGFBP2","MGP","MYH11","DCN","TNFRSF11B")
genes_to_show <- c("DCN","LUM","MMP2","ACTA2","TNFRSF11B","FBLN1")

merge_expr <- data.frame()

for (i in lapply(genes_to_show, func1,"ds0",ds0data))
{
  merge_expr <- rbind(merge_expr,i)
}
for (i in lapply(genes_to_show, func1,"ds2",ds2data))
{
  merge_expr <- rbind(merge_expr,i)
}

rownames(merge_expr) <- NULL

Data_summary <- Rmisc::summarySE(merge_expr, measurevar="expr", groupvars=c("sample","gene"))
head(Data_summary)

ggobj <- ggplot(merge_expr,aes(x = gene, y = expr,fill = sample)) +
  geom_split_violin(trim= F, color="white", scale = "area") + 
  geom_point(data = Data_summary,aes(x = gene, y= expr), pch=19,
             position=position_dodge(0.2),size= 1) + #绘制均值位置
  geom_errorbar(data = Data_summary, aes(ymin = expr-ci, ymax= expr+ci), 
                width= 0.05, 
                position= position_dodge(0.2), #误差线位置，和均值位置相匹配
                color="black",
                alpha = 0.7,
                size= 0.5) +
  scale_fill_manual(values = c("#b1d6fb", "#fd9999"))+ 
  labs(y=("Log2 expression"),x=NULL,title = "Split violin") + 
  theme_classic()+ mytheme + stat_compare_means(aes(group = sample),
                     label = "p.format",
                     method = "wilcox.test",
                     label.y = max(merge_expr$expr),
                      hide.ns = F)
ggobj
ggsave("./scmap/scmapsupds2tods0.svg", device = svg, plot = ggobj, height = 6, width = 10)

Appendix ds1

构造ref ds1

ds1 <- readRDS("ds1.rds")
ref_sce <- as.SingleCellExperiment(ds1)
logcounts(ref_sce) <- log2(counts(ref_sce) + 1)

counts(ref_sce) <- as.matrix(counts(ref_sce))
# normcounts(ref_sce) <- as.matrix(normcounts(ref_sce))
logcounts(ref_sce) <- as.matrix(logcounts(ref_sce))

rowData(ref_sce)$feature_symbol <- rownames(ref_sce)
ref_sce <- ref_sce[!duplicated(rownames(ref_sce)), ]
ref_sce <- selectFeatures(ref_sce, suppress_plot = FALSE)

Warning: 'isSpike' is deprecated.
See help("Deprecated")

ref_sce <- indexCell(ref_sce)

Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)

环回ds1

scmapCell_results <- scmapCell(ref_sce, list(ds1 = metadata(ref_sce)$scmap_cell_index))

Warning: stack imbalance in '<-', 2 then 1

scmapCell_clusters <- scmapCell2Cluster(
  scmapCell_results, 
  list(as.character(colData(ref_sce)$Classification1)))

temp <- data.frame(scmapCell_clusters$scmap_cluster_labs[,"ds1"],row.names = colnames(ds1))
Idents(ds1) <- temp
ggsave("./scmap/scmap_ds1tods1.svg", device = svg, width = 6, height = 4, plot = umapplot(ds1))

fig <- plot_ly(data.frame(table(temp)), labels = ~temp, values = ~Freq, type = 'pie',
        textposition = 'inside',
        textinfo = 'label+percent+value',
        insidetextfont = list(color = '#000000'),
        hoverinfo = 'text',
        text = ~paste0('cell numbers: ', Freq),
        marker = list(colors = colors_list,
                      line = list(color = '#FFFFFF', width = 0)),
        showlegend = FALSE) %>% layout(title = 'scmap_ds1tods1',
         xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
         yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE), 
         font = list(family = "Arial", size = 25, color = "black"))

fig

计算ARI = 0.7935582 ds1

mclust::adjustedRandIndex(temp[,1], ds1$Classification1)

[1] 0.7935582

confusing matrix

conmat <- table(as.character(ds1$Classification1), temp[,1], dnn=c("true","pre"))
conmat_prop <- prop.table(conmat, 1)
conmat_prop

              pre
true           Fibromyocyte        SMC1        SMC2  unassigned     Unknown
  Fibromyocyte  0.745973646 0.031478770 0.003660322 0.218887262 0.000000000
  SMC1          0.002773376 0.939778130 0.000792393 0.056656101 0.000000000
  SMC2          0.007518797 0.005012531 0.822055138 0.165413534 0.000000000
  Unknown       0.000000000 0.000000000 0.000000000 0.115384615 0.884615385

confuse_bubblemat(conmat_prop, rownames(conmat_prop),  colnames(conmat_prop),"ds1_scmap")

XGBoost ds1

Idents(ds1) <- ds1$Classification1
ds1 <- RenameIdents(ds1, 'Unknown' = 0, 'SMC1' = 1, 'Fibromyocyte' = 2, 'SMC2' = 3)
ds1_data <- get_data_table(ds1, highvar = F, type = "data")
ds1_label <- as.numeric(as.character(Idents(ds1)))

set.seed(7)
index <- c(1:dim(ds1_data)[2]) %>% sample(ceiling(0.3*dim(ds1_data)[2]), replace = F, prob = NULL)
colnames(ds1_data) <- NULL
ds1_train_data <- list(data = t(as(ds1_data[,-index],"dgCMatrix")), label = ds1_label[-index])
ds1_test_data <- list(data = t(as(ds1_data[,index],"dgCMatrix")), label = ds1_label[index])
ds1_train <- xgb.DMatrix(data = ds1_train_data$data,label = ds1_train_data$label)
ds1_test <- xgb.DMatrix(data = ds1_test_data$data,label = ds1_test_data$label)

watchlist <- list(train = ds1_train, eval = ds1_test)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds1))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')
bst_model <- xgb.train(xgb_param, ds1_train, nrounds = 100, watchlist, verbose = 0)

adjustedRandIndex(ds1_test_data$label, predict_ds1_test) #ARI = 

[1] 0.8385574

XGBoost

Idents(ds0) <- ds0$Classification1
ds0 <- RenameIdents(ds0, 'Fibroblast' = 0, 'SMC' = 1, 'Fibromyocyte' = 2, 'Pericyte' = 3, 'Unknown' = 4)
ds0_data <- get_data_table(ds0, highvar = T, type = "data")
ds0_label <- as.numeric(as.character(Idents(ds0)))
ds0_ARI <- list()

for(i in seq(1:10))
{
  set.seed(17*i)
  index <- c(1:dim(ds0_data)[2]) %>% sample(ceiling(0.3*dim(ds0_data)[2]), replace = F, prob = NULL)
  colnames(ds0_data) <- NULL
  ds0_train_data <- list(data = t(as(ds0_data[,-index],"dgCMatrix")), label = ds0_label[-index])
  ds0_test_data <- list(data = t(as(ds0_data[,index],"dgCMatrix")), label = ds0_label[index])
  ds0_train <- xgb.DMatrix(data = ds0_train_data$data,label = ds0_train_data$label)
  ds0_test <- xgb.DMatrix(data = ds0_test_data$data,label = ds0_test_data$label)
  
  watchlist <- list(train = ds0_train, eval = ds0_test)
  xgb_param <- list(eta = 0.2, max_depth = 6, 
                    subsample = 0.6,  num_class = length(table(Idents(ds0))),
                    objective = "multi:softmax", eval_metric = 'mlogloss')
  bst_model <- xgb.train(xgb_param, ds0_train, nrounds = 100, watchlist, verbose = 0)
  predict_ds0_test <- round(predict(bst_model, newdata = ds0_test))
  ds0_ARI[i] <- adjustedRandIndex(ds0_test_data$label, predict_ds0_test)
}

Idents(ds1) <- ds1$Classification1
ds1 <- RenameIdents(ds1, 'Unknown' = 0, 'SMC1' = 1, 'Fibromyocyte' = 2, 'SMC2' = 3)

ds1_data <- get_data_table(ds1, highvar = T, type = "data")
ds1_label <- as.numeric(as.character(Idents(ds1)))
ds1_ARI <- list()

for(i in seq(1:10))
{
  set.seed(17*i)
  index <- c(1:dim(ds1_data)[2]) %>% sample(ceiling(0.3*dim(ds1_data)[2]), replace = F, prob = NULL)
  colnames(ds1_data) <- NULL
  ds1_train_data <- list(data = t(as(ds1_data[,-index],"dgCMatrix")), label = ds1_label[-index])
  ds1_test_data <- list(data = t(as(ds1_data[,index],"dgCMatrix")), label = ds1_label[index])
  ds1_train <- xgb.DMatrix(data = ds1_train_data$data,label = ds1_train_data$label)
  ds1_test <- xgb.DMatrix(data = ds1_test_data$data,label = ds1_test_data$label)
  
  watchlist <- list(train = ds1_train, eval = ds1_test)
  xgb_param <- list(eta = 0.2, max_depth = 6, 
                    subsample = 0.6,  num_class = length(table(Idents(ds1))),
                    objective = "multi:softmax", eval_metric = 'mlogloss')
  bst_model <- xgb.train(xgb_param, ds1_train, nrounds = 100, watchlist, verbose = 0)
  predict_ds1_test <- round(predict(bst_model, newdata = ds1_test))
  ds1_ARI[i] <- adjustedRandIndex(ds1_test_data$label, predict_ds1_test)
}

Idents(ds2) <- ds2$Classification1
ds2 <- RenameIdents(ds2, 'SMC1' = 0, 'Fibromyocyte' = 1, 'Pericyte' = 2, 'Fibroblast' = 3, 'SMC2' = 4)

ds2_data <- get_data_table(ds2, highvar = T, type = "data")
ds2_label <- as.numeric(as.character(Idents(ds2)))
ds2_ARI <- list()

for(i in seq(1:10))
{
  set.seed(17*i)
  index <- c(1:dim(ds2_data)[2]) %>% sample(ceiling(0.3*dim(ds2_data)[2]), replace = F, prob = NULL)
  colnames(ds2_data) <- NULL
  ds2_train_data <- list(data = t(as(ds2_data[,-index],"dgCMatrix")), label = ds2_label[-index])
  ds2_test_data <- list(data = t(as(ds2_data[,index],"dgCMatrix")), label = ds2_label[index])
  ds2_train <- xgb.DMatrix(data = ds2_train_data$data,label = ds2_train_data$label)
  ds2_test <- xgb.DMatrix(data = ds2_test_data$data,label = ds2_test_data$label)
  
  watchlist <- list(train = ds2_train, eval = ds2_test)
  xgb_param <- list(eta = 0.2, max_depth = 6, 
                    subsample = 0.6,  num_class = length(table(Idents(ds2))),
                    objective = "multi:softmax", eval_metric = 'mlogloss')
  bst_model <- xgb.train(xgb_param, ds2_train, nrounds = 100, watchlist, verbose = 0)
  predict_ds2_test <- round(predict(bst_model, newdata = ds2_test))
  ds2_ARI[i] <- adjustedRandIndex(ds2_test_data$label, predict_ds2_test)
}

构造ref ds0

ref_sce <- as.SingleCellExperiment(ds0)
logcounts(ref_sce) <- log2(counts(ref_sce) + 1)
counts(ref_sce) <- as.matrix(counts(ref_sce))
logcounts(ref_sce) <- as.matrix(logcounts(ref_sce))
rowData(ref_sce)$feature_symbol <- rownames(ref_sce)
ref_sce <- ref_sce[!duplicated(rownames(ref_sce)), ]
ref_sce <- selectFeatures(ref_sce, suppress_plot = FALSE)

Warning: 'isSpike' is deprecated.
See help("Deprecated")

scmapARI_ds0 <- list()

for(i in seq(1:10))
{
  set.seed(17*i)
  ref_sce <- indexCell(ref_sce)
  scmapCell_results <- scmapCell(ref_sce, list(ds0 = metadata(ref_sce)$scmap_cell_index))
  scmapCell_clusters <- scmapCell2Cluster(
    scmapCell_results, 
    list(as.character(colData(ref_sce)$Classification1)))
  temp <- data.frame(scmapCell_clusters$scmap_cluster_labs[,"ds0"],row.names = colnames(ds0))
  scmapARI_ds0[i] <- mclust::adjustedRandIndex(temp[,1], ds0$Classification1)
}

Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)
Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)
Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)
Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)
Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)
Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)
Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)
Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)
Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)
Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)

ARI ds1

ref_sce <- as.SingleCellExperiment(ds1)
logcounts(ref_sce) <- log2(counts(ref_sce) + 1)
counts(ref_sce) <- as.matrix(counts(ref_sce))
logcounts(ref_sce) <- as.matrix(logcounts(ref_sce))
rowData(ref_sce)$feature_symbol <- rownames(ref_sce)
ref_sce <- ref_sce[!duplicated(rownames(ref_sce)), ]
ref_sce <- selectFeatures(ref_sce, suppress_plot = FALSE)

Warning: 'isSpike' is deprecated.
See help("Deprecated")

scmapARI_ds1 <- list()

for(i in seq(1:10))
{
  set.seed(17*i)
  ref_sce <- indexCell(ref_sce)
  scmapCell_results <- scmapCell(ref_sce, list(ds1 = metadata(ref_sce)$scmap_cell_index))
  scmapCell_clusters <- scmapCell2Cluster(
    scmapCell_results, 
    list(as.character(colData(ref_sce)$Classification1)))
  temp <- data.frame(scmapCell_clusters$scmap_cluster_labs[,"ds1"],row.names = colnames(ds1))
  scmapARI_ds1[i] <- mclust::adjustedRandIndex(temp[,1], ds1$Classification1)
}

Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)
Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)
Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)
Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)
Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)
Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)
Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)
Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)
Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)
Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)

ARI ds2

ref_sce <- as.SingleCellExperiment(ds2)
logcounts(ref_sce) <- log2(counts(ref_sce) + 1)
counts(ref_sce) <- as.matrix(counts(ref_sce))
logcounts(ref_sce) <- as.matrix(logcounts(ref_sce))
rowData(ref_sce)$feature_symbol <- rownames(ref_sce)
ref_sce <- ref_sce[!duplicated(rownames(ref_sce)), ]
ref_sce <- selectFeatures(ref_sce, suppress_plot = FALSE)

Warning: 'isSpike' is deprecated.
See help("Deprecated")

scmapARI_ds2 <- list()

for(i in seq(1:10))
{
  set.seed(17*i)
  ref_sce <- indexCell(ref_sce)
  scmapCell_results <- scmapCell(ref_sce, list(ds2 = metadata(ref_sce)$scmap_cell_index))
  scmapCell_clusters <- scmapCell2Cluster(
    scmapCell_results, 
    list(as.character(colData(ref_sce)$Classification1)))
  temp <- data.frame(scmapCell_clusters$scmap_cluster_labs[,"ds2"],row.names = colnames(ds2))
  scmapARI_ds2[i] <- mclust::adjustedRandIndex(temp[,1], ds2$Classification1)
}

Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)
Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)
Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)
Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)
Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)
Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)
Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)
Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)
Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)
Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)

ARI performance

ARI    scmap     XGBoost
ds0   0.8925608 0.9316151
ds1   0.7935582 0.8385574
ds2   0.776329  0.9002053

##SMC2的鉴别

ds1 project to ds2

构造ref

ds2 <- readRDS("ds2.rds")
ref_sce <- as.SingleCellExperiment(ds2)
logcounts(ref_sce) <- log2(counts(ref_sce) + 1)

counts(ref_sce) <- as.matrix(counts(ref_sce))
# normcounts(ref_sce) <- as.matrix(normcounts(ref_sce))
logcounts(ref_sce) <- as.matrix(logcounts(ref_sce))

rowData(ref_sce)$feature_symbol <- rownames(ref_sce)
ref_sce <- ref_sce[!duplicated(rownames(ref_sce)), ]
ref_sce <- selectFeatures(ref_sce, suppress_plot = FALSE)

Warning: 'isSpike' is deprecated.
See help("Deprecated")

ref_sce <- indexCell(ref_sce)

Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)

构造query

ds1 <- readRDS("ds1.rds")
query_sce <- as.SingleCellExperiment(ds1)
logcounts(query_sce) <- log2(counts(query_sce) + 1)

counts(query_sce) <- as.matrix(counts(query_sce))
# normcounts(query_sce) <- as.matrix(normcounts(query_sce))
logcounts(query_sce) <- as.matrix(logcounts(query_sce))

rowData(query_sce)$feature_symbol <- rownames(query_sce)
query_sce <- query_sce[!duplicated(rownames(query_sce)), ]
query_sce <- selectFeatures(query_sce, suppress_plot = FALSE)

Warning: 'isSpike' is deprecated.
See help("Deprecated")

query_sce <- indexCell(query_sce)

Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)

scmapCell_results <- scmapCell(query_sce, list(ds2 = metadata(ref_sce)$scmap_cell_index))
scmapCell_clusters <- scmapCell2Cluster(
  scmapCell_results, 
  list(as.character(colData(ref_sce)$Classification1)))
temp <- data.frame(scmapCell_clusters$scmap_cluster_labs[,"ds2"], row.names = colnames(ds1))
Idents(ds1) <- temp
ggsave("./scmap/scmap_ds1tods2.svg", device = svg, width = 6, height = 4, plot = umapplot(ds1))

Warning: Using `as.character()` on a quosure is deprecated as of rlang 0.3.0.
Please use `as_label()` or `as_name()` instead.
This warning is displayed once per session.

fig <- plot_ly(data.frame(table(temp)), labels = ~temp, values = ~Freq, type = 'pie',
        textposition = 'inside',
        textinfo = 'label+percent+value',
        insidetextfont = list(color = '#000000'),
        hoverinfo = 'text',
        text = ~paste0('cell numbers: ', Freq),
        marker = list(colors = colors_list,
                      line = list(color = '#FFFFFF', width = 0)),
        showlegend = FALSE) %>% layout(title = 'scmap_ds1tods2',
         xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
         yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE), 
         font = list(family = "Arial", size = 25, color = "black"))

fig

SMC2 比较 scmap vs xgboost vs unsupervised

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